# Under the fasstr Hood

The Flow Analysis Summary Statistics Tool for R (‘fasstr’) is a set of R functions to tidy, summarize, analyze, trend, and visualize streamflow data. This package summarizes continuous daily mean streamflow data into various daily, monthly, annual, and long-term statistics, completes annual trends and frequency analyses, in both table and plot formats.

This vignette guide contains a look at the steps and internal functions used within the various ‘fasstr’ functions. It includes descriptions of the function and steps on data importing, cleaning and wrangling, filtering, analyzing, and plotting. It also describes some of the functions in the internal utils.R file and the packages required to use the ‘fasstr’ package.

## Introduction

‘fasstr’ utilizes tidy coding to utilize many efficient and useful packages and functions; the tidyverse packages tidy, dplyr, ggplot2 are used frequently, amongst others for specific functions. Because of this, ‘fasstr’ also exports the pipe “%>%” to assist in using ‘fasstr’ in a tidy code routine.

This vignette is broken into the following sections that describe some of the workflows involved in the ‘fasstr’ functions:

1. Data Importing and Checking
2. Cleaning Functions (add_ and fill_)
3. Analysis Functions (calc_ and compute_)
4. Plotting Functions (plot_)
5. Internal Functions (utils.R file)
6. Package Dependencies

## 1. Data Importing and Checking

The ‘fasstr’ functions primarily require daily mean streamflow data in cubic metres per second. As such, columns of dates and values are required. Many ‘fasstr’ functions can analyze multiple stations (using grouping functions) so a column to group data by (typically station numbers) can be provided. Other types of data can be used (climate, water level, etc), but plots and some conversions are based on the units of discharge. As there are two ways to provide daily data to the ‘fasstr’ functions, the “data” and the “station_number” arguments, there are several internal functions stored in the utils.R file that checks the data and formats the columns to ensure the data is prepared for the analysis. The following describes the steps to ensure data is consistently set up for analysis.

The first step is to import daily data. The internal flowdata_import() function provides checks on the “data” and “station_number” arguments, and either extracts daily data from HYDAT (returns an error if the number isn’t in HYDAT) using the ‘tidyhydat’ package, or makes sure that “data” is in fact a data frame.

At this point in a ‘fasstr’ function, all the column names of the data are saved as in a vector and any grouping (tidy grouping) is removed. The column names are saved as they may be changed in the following steps and the original names will be returned at the end of the analysis. Any grouping is removed to allow for the appropriate grouping of the analysis. For the cleaning functions (add_ and fill_) any grouping will be returned.

The next step is to ensure the column names of dates, values, and groups are consistent as “Date”, “Value”, and “STATION_NUMBER” (to match the HYDAT outputs), respectively, for the analysis. This is required as grouping and summarizing functions in the following steps use these specific column names, and provided data may not use the same names. The facilitate the column renaming and checking, there are several internal functions in the utils.R. For each of the three columns there is a function that checks if it exists, the proper formatting, and renames the columns to the appropriate name. These functions are format_dates_col(), format_values_col(), and format_groups_col(). If no grouping is provided, a fake STATION_NUMBER of “XXXXXXX” is provided (and removed at the end of the ‘fasstr’ function). As all three columns are required for many ‘fasstr’ functions, there is a formal_all_cols() that uses all three functions, with an option to remove all other columns.

At this point in a function there is now a data frame of data, usually called ‘flow_data’ that has three columns of “Date”, “Value”, and “STATION_NUMBER”, that is ready to be cleaned for analysis.

Note: some functions use different data sources than daily data. The compute_HYDAT_peaks_frequencies() does not permit the “data” argument and just extracts data from HYDAT (and sets it up appropriately) and the compute_frequency_analysis() function requires a unique dataset (see ?compute_frequency_analysis for more information on the function).

The following is an example of these first steps (from calc_annual_stats()):

    # Check if data is provided and import it
    flow_data <- flowdata_import(data = data,
                                 station_number = station_number)

    # Save the original columns (to check for STATION_NUMBER col at end) and ungroup if necessary
    orig_cols <- names(flow_data)
    flow_data <- dplyr::ungroup(flow_data)

    # Check and rename columns
    flow_data <- format_all_cols(data = flow_data,
                                 dates = as.character(substitute(dates)),
                                 values = as.character(substitute(values)),
                                 groups = as.character(substitute(groups)),
                                 rm_other_cols = TRUE)

## 2. Cleaning Functions (add_ and fill_)

The ‘fasstr’ cleaning functions (start with add_ or fill_) just add rows or columns of data so there are fewer steps than the other ‘fasstr’ functions.

After the data has been prepped with the appropriate columns, the columns are added with their equations or other functions, or dates are filled with NA if they are missing. Then if any of the column names were changed in the formatting, they are returned to their original names, and if there was a grouping beforehand, that grouping is returned.

The following is an example of remaining steps of a cleaning function (from add_daily_yield()):

    ## SET UP BASIN AREA
    suppressWarnings(flow_data <- add_basin_area(flow_data, basin_area = basin_area))
    flow_data$Basin_Area_sqkm_temp <- flow_data$Basin_Area_sqkm

    ## ADD YIELD COLUMN
    flow_data <- dplyr::mutate(flow_data, Yield_mm = Value * 86400 / (Basin_Area_sqkm_temp * 1000))

    # Return the original names of the Date and Value columns
    names(flow_data)[names(flow_data) == "Value"] <- as.character(substitute(values))
    names(flow_data)[names(flow_data) == "STATION_NUMBER"] <- as.character(substitute(groups))

    ## Reformat to original names and groups
    ## -------------------------------------

    # Return columns to original order plus new column
    if("Yield_mm" %in% orig_cols){
      flow_data <-  flow_data[, c(orig_cols)]
    } else {
      flow_data <-  flow_data[, c(orig_cols, paste("Yield_mm"))]
    }

    dplyr::as_tibble(flow_data)

## 3. Analysis Functions (calc_ and compute_)

The ‘fasstr’ analysis functions (start with calc_ or compute_ or screen_) require steps of cleaning (adding appropriate columns), filtering, analyzing and any final data wrangling.

#### Data Cleaning and Preparing

After the data has been prepped with the appropriate columns, additional columns of dates (years, months, days), rolling days, basin areas, yields or volumes may be added, depending on the analysis. Dates with no data are also filled with NA values so that complete years can be analyzed (and appropriate warnings made if NA’s are produced). To facilitate these steps there is an internal function in the utils.R file called analysis_prep(). This function will fill the missing dates and add columns of Years, Months, WaterYears, DayofYears, and Dates to the data frame. It also creates a column of AnalysisDate if necessary, that extracts the dates and converts them into a single year for columns or plotting. This is to provide consistency to the analysis functions (as Year or WaterYear could be used depending on the water_year_start argument choice). The analysis date columns are ultimately returned to their original names (Year, DayofYear, Date) in the results. Any other columns are added as necessary using the necessary cleaning functions.

For some functions a “RollingValue” column is created for the statistics calculations so that a roll_day value of other than 1 can be used. For this, the add_rolling_days() function creates an additional column based on the users selection and is renamed to “RollingValue”.

At this point in a function the data frame of data, flow_data, has three columns of “Date”, “Value” or “RollingValue”, and “STATION_NUMBER”, and any other date and necessary column required to be filtered for analysis.

The following is an example of the cleaning steps (from calc_annual_stats()):

    # Fill missing dates, add date variables
    flow_data <- analysis_prep(data = flow_data,
                               water_year_start = water_year_start)

    # Add rolling means to end of dataframe
    flow_data <- add_rolling_means(data = flow_data, roll_days = roll_days, roll_align = roll_align)
    colnames(flow_data)[ncol(flow_data)] <- "RollingValue"

#### Data Filtering

With all the necessary columns of data for analysis, the data can be filtered for the years, months, dates, etc as specified in the ‘fasstr’ function arguments. These are mostly completed with simple tidy filtering functions. However when using the complete_years argument in some of the ‘fasstr’ functions, there is an internal function in the utils.R file called filter_complete_yrs() that removes data from years with incomplete data.

At this point in a function the data frame of data, flow_data, has three columns of “Date”, “Value”, and “STATION_NUMBER”, and any other date and necessary column, and is filtered for the analysis.

The following is an example of the filtering steps (from calc_annual_stats()):

    # Filter for the selected year (remove excluded years after)
    flow_data <- dplyr::filter(flow_data, AnalysisYear >= start_year & AnalysisYear <= end_year)
    flow_data <- dplyr::filter(flow_data, Month %in% months)

#### Calculating Statistics

Now that the data contains all the necessary data to summarize, using the tidy methods of the tidyverse (predominately tidyr and dplyr packages), data is grouped by the necessary variables. Data is usually grouped by STATION_NUMBER and then whatever time-frame variable the function calls for (years, months, days, etc).

Then the data is typically analyzed using the dplyr::summarize() function (for means, median, minimums, etc). If an analysis considers if missing data is available or not, then the na.rm argument is set to the ignore_missing function provided in the ‘fasstr’ functions. If NAN or Inf values are produced in the results, then they are replaced with NA for consistency. If percentiles are being calculated, another code chunk will loop through each percentile selected and attach to the data.

Data is then ungrouped to allow for further data wrangling and outputting.

The following is an example of the calculation steps (from calc_annual_stats()):

    # Calculate basic stats
    annual_stats <-   dplyr::summarize(dplyr::group_by(flow_data, STATION_NUMBER, AnalysisYear),
                                       Mean = mean(RollingValue, na.rm = ignore_missing),
                                       Median = stats::median(RollingValue, na.rm = ignore_missing),
                                       Maximum = max (RollingValue, na.rm = ignore_missing),
                                       Minimum = min (RollingValue, na.rm = ignore_missing))
    annual_stats <- dplyr::ungroup(annual_stats)

    #Remove Nans and Infs
    annual_stats$Mean[is.nan(annual_stats$Mean)] <- NA
    annual_stats$Maximum[is.infinite(annual_stats$Maximum)] <- NA
    annual_stats$Minimum[is.infinite(annual_stats$Minimum)] <- NA

    # Calculate annual percentiles
    if(!all(is.na(percentiles))) {
      for (ptile in percentiles) {
        # Calculate percentiles
        annual_stats_ptile <- dplyr::summarise(dplyr::group_by(flow_data, STATION_NUMBER, AnalysisYear),
                                               Percentile = stats::quantile(RollingValue, ptile / 100, na.rm = TRUE))
        annual_stats_ptile <- dplyr::ungroup(annual_stats_ptile)
        names(annual_stats_ptile)[names(annual_stats_ptile) == "Percentile"] <- paste0("P", ptile)

        # Merge with stats
        annual_stats <- merge(annual_stats, annual_stats_ptile, by = c("STATION_NUMBER", "AnalysisYear"))

        # Remove percentile if mean is NA (workaround for na.rm=FALSE in quantile)
        annual_stats[, ncol(annual_stats)] <- ifelse(is.na(annual_stats$Mean), NA, annual_stats[, ncol(annual_stats)])
      }
    }

#### Final Data Wrangling

To finalize the data results there are usually some final data filtering or wrangling steps. Some of these include some additional filtering (usually when using the exclude_years argument; replaces data with NA), providing warnings if NA’s are produced in the data, renaming the analysis date columns (AnalysisYear to Year; as reversing above cleaning functions), and returning the names of the groups (i.e. STATION_NUMBER column) if different, or removing it if it wasn’t provided (as listed in the original column names vector described above).

If the option to transpose the results data was set to TRUE with the transpose argument, then the data is gathered and spread such that each statistics is a single row as opposed to a column in the original results.

After all wrangling, the final results are returned from the function as a tibble.

The following is an example of the final wrangling steps (from calc_annual_stats()):

    # Rename year column
    annual_stats <- dplyr::rename(annual_stats, Year = AnalysisYear)

    # Remove selected excluded years
    annual_stats[annual_stats$Year %in% exclude_years, -(1:2)] <- NA

    # If transpose if selected
    if (transpose) {
      # Get list of columns to order the Statistic column after transposing
      stat_levels <- names(annual_stats[-(1:2)])

      # Transpose the columns for rows
      annual_stats <- tidyr::gather(annual_stats, Statistic, Value, -STATION_NUMBER, -Year)
      annual_stats <- tidyr::spread(annual_stats, Year, Value)

      # Order the columns
      annual_stats$Statistic <- factor(annual_stats$Statistic, levels = stat_levels)
      annual_stats <- dplyr::arrange(annual_stats, STATION_NUMBER, Statistic)
    }

    # Give warning if any NA values
    missing_values_warning(annual_stats[, 3:ncol(annual_stats)])

    # Recheck if station_number/grouping was in original data and rename or remove as necessary
    if(as.character(substitute(groups)) %in% orig_cols) {
      names(annual_stats)[names(annual_stats) == "STATION_NUMBER"] <- as.character(substitute(groups))
    } else {
      annual_stats <- dplyr::select(annual_stats, -STATION_NUMBER)
    }

    dplyr::as_tibble(annual_stats)

## 4. Plotting Functions (plot_)

The ‘fasstr’ plotting functions (start with plot_) take the data calculated in the analysis functions and then plot the data with set or custom plotting templates.

#### Calculating Statistics

For the plotting functions data is typically calculated from the other calc_ ‘fasstr’ functions and then wrangled into proper formatting. Before calculating the data, various checks are performed on the arguments and then the flowdata_import() is first used to check the ‘data’ or ‘station_data’ selections. Then the values, dates, and groups columns are formatted properly and all other columns removed using the format_all_cols() internal function. This data frame and the function arguments are then passed onto the ‘fasstr’ calc_ function. Any final data wrangling is also then completed to set it up for the plotting functions. Some NA values may be replaced with substitute values to allow the plot to be produced along with the appropriate data warnings.

#### Plotting Statistics

Before plotting, some variables are created based on the custom options provided in the arguments (axis labels etc).

As there is the potential for many plots to be produced in the ‘fasstr’ functions, a tidy method was used to create multiple plots simultaneously in an efficient process using dplyr, tidyr, purrr, and ggplot2 packages to create a tibble of plots. See this webpage for more information on the method. For ‘fasstr’ plotting functions, this entails creating a single tibble of groups (STATION_NUMBERs), data, and plots. First the data is grouped by the grouping (typically STATION_NUMBERs) using the dplyr::group() function; this will create a single plot for each grouping. Then using tidyr::nest() a tibble is created with two columns, the first being the STATION_NUMBER and the second being a tibble of data for each STATION_NUMBER (a tibble of tibbles). Then using dplyr::mutate() function a column of ggplots is created; and using purrr::map2() all of the plots can be created simultaneously (thus saving time in the function). As this type of table isn’t entirely user-friendly, each plot is extracted into a list and named appropriately (typically by each grouping and then the statistic).

The following is an example of the process of the creating the plots (with simplified plotting).

    # Calculate the statistics
    annual_stats <- calc_annual_stats(station_number = c("08NM116", "08NM240"),
                                      start_year = 1985, end_year = 2015)

    # Wrangle statistics for plotting
    annual_stats <- tidyr::gather(annual_stats, Statistic, Value, -Year, -STATION_NUMBER)

    # Group data by grouping
    tidy_plots <- dplyr::group_by(annual_stats, STATION_NUMBER)

    # Create a tibble with a column of STATION_NUMBERs and a column of data for each STATION_NUMBER
    tidy_plots <- tidyr::nest(tidy_plots)

    # Create a new column of plots using mutate and purrr::map2
    tidy_plots <- dplyr::mutate(tidy_plots,
                                plot = purrr::map2(data, STATION_NUMBER,
                                                   ~ggplot2::ggplot(data = ., ggplot2::aes(x = Year, y = Value, color = Statistic)) +
                                                     ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5)) +
                                                     ggplot2::geom_line(alpha = 0.5, na.rm = TRUE) +
                                                     ggplot2::geom_point(na.rm = TRUE) +
                                                     ggplot2::ylab("Discharge (cms)")

                                ))

    # Create a list of named plots extracted from the tibble
    plots <- tidy_plots$plot
    if (nrow(tidy_plots) == 1) {
      names(plots) <- "Annual_Statistics"
    } else {
      names(plots) <- paste0(tidy_plots$STATION_NUMBER, "_Annual_Statistics")
    }

    # Return the plots
    plots

    $`08NM116_Annual_Statistics`

    $`08NM240_Annual_Statistics`

## 5. Internal Functions (utils.R file)

Within the package there is an R file called utils.R that contains a list of functions that are used repeatedly throughout the various ‘fasstr’ functions to minimize repetition. These functions are used internally and are not exported from the package.

At the top of the file are several data formatting and wrangling functions:

- flowdata_import() - checks if either `station_number` or `data` arguments are provided; extracts HYDAT data or sets up data for next steps.
- format_dates_col() - checks for proper dates formats in column; renames columns to “Date” if not.
- format_values_col() - checks for proper numeric formats in column; renames columns to “Value” if not.
- format_groups_col() - checks for proper formats in column; renames columns to “STATION_NUMBER” if not; if no groups provided, a temporary STATION_NUMBER of “XXXXXXX” is created for analysis.
- format_all_cols() - checks for proper formats of all dates, values, and groups columns; uses all three functions listed above; option to remove all other columns if `rm_other_cols = TRUE`.
- analysis_prep() - fills missing dates with NA; adds dates columns of Years, day of years, and dates; formatted to calendar or water year as specified.
- filter_complete_yrs() - filters data for years with which there is only complete annual data; required for some analyses.

Below these formatting and data wrangling functions are many functions used to check argument and provide warnings when certain data is present. See the file for examples of these internal functions.

## 6. Package Dependencies

The following list contains the packages required to complete many of the ‘fasstr’ functions:

- dplyr - used for many tidy functions (grouping, summarizing, etc)
- e1071 - used in the frequency analyses for determining skewness for the fitting distributions
- fitdistrplus - used in the frequency analyses for fitting data to the distributions
- ggplot2 - used for plotting the data
- lubridate - used for easy creation of date columns
- PearsonDS - used in the frequency analyses for fitting data to the distributions
- plyr - used in the frequency analyses to handle data
- purrr - used in the plotting functions to plot many plots simultaneously
- RcppRoll - used to add rolling means
- scales - used in the plotting functions to customize plots
- tidyhydat - used to extract data from a HYDAT database
- tidyr - used for many tidy functions (gather, spread, etc)
- openxlsx - used to write data as Excel files in the write_ functions
- zyp - used in the trending function to calculate the trends